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FREDA: Flexible Relation Extraction Data Annotation. (arXiv:2204.07150v1 [cs.CL])
April 15, 2022, 1:11 a.m. | Michael Strobl, Amine Trabelsi, Osmar Zaiane
cs.CL updates on arXiv.org arxiv.org
To effectively train accurate Relation Extraction models, sufficient and
properly labeled data is required. Adequately labeled data is difficult to
obtain and annotating such data is a tricky undertaking. Previous works have
shown that either accuracy has to be sacrificed or the task is extremely
time-consuming, if done accurately. We are proposing an approach in order to
produce high-quality datasets for the task of Relation Extraction quickly.
Neural models, trained to do Relation Extraction on the created datasets,
achieve very …
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